{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align = 'center'> 学会观察 </h1>\n",
    "\n",
    "<h2 align = 'center'> 第12部分:让我们变得贪婪</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# 启用PyLab，将图形嵌入到Notebook中\n",
    "%pylab inline\n",
    "\n",
    "# 导入支持函数和pickle模块\n",
    "from supportFunctions import *\n",
    "import pickle as cPickle\n",
    "\n",
    "# 定义 pickle 文件的文件名\n",
    "pickleFileName = 'data/fingerDataSet' + '.pickle'\n",
    "\n",
    "# 以二进制读模式打开 pickle 文件，使用 latin1 编码\n",
    "with open(pickleFileName, 'rb') as pickleFile:\n",
    "    # 使用 pickle.load() 从 pickle 文件中加载数据\n",
    "    data = cPickle.load(pickleFile, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 选择3个图像作为训练数据集\n",
    "trainingExampleIndices = [7, 30, 46]\n",
    "trainingExamples = [data[index] for index in trainingExampleIndices]\n",
    "trainX, trainY = extractExamplesFromList(trainingExamples, whichImage='image1bit', dist=4)\n",
    "\n",
    "# 选择2个图像作为测试数据集\n",
    "testingExampleIndices = [40, 41]\n",
    "testingExamples = [data[index] for index in testingExampleIndices]\n",
    "testX, testY = extractExamplesFromList(testingExamples, whichImage='image1bit', dist=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 再次找到最佳3像素规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed = 48.967 seconds.\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "rules = []  # 存储规则\n",
    "numErrors = []  # 存储每个规则对应的错误数量\n",
    "startTime = time.time()\n",
    "\n",
    "# 遍历所有三元组组合\n",
    "for c in itertools.combinations(range(81), 3):\n",
    "    i = c[0]; j = c[1]; k = c[2]\n",
    "\n",
    "    # 不包含 i 和 j 和 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 0, trainX[:, j] == 0), trainX[:, k] == 0)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 0, 0, 0])\n",
    "\n",
    "    # 包含 i 和不包含 j 和 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 0, trainX[:, j] == 0), trainX[:, k] == 1)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 0, 0, 1])\n",
    "\n",
    "    # 不包含 i 和包含 j 和不包含 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 0, trainX[:, j] == 1), trainX[:, k] == 0)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 0, 1, 0])\n",
    "\n",
    "    # 包含 i 和 j 和不包含 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 0, trainX[:, j] == 1), trainX[:, k] == 1)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 0, 1, 1])\n",
    "\n",
    "    # 不包含 i 和不包含 j 和包含 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 1, trainX[:, j] == 0), trainX[:, k] == 0)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 1, 0, 0])\n",
    "\n",
    "    # 包含 i 和不包含 j 和包含 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 1, trainX[:, j] == 0), trainX[:, k] == 1)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 1, 0, 1])\n",
    "\n",
    "    # 不包含 i 和包含 j 和 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 1, trainX[:, j] == 1), trainX[:, k] == 0)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 1, 1, 0])\n",
    "\n",
    "    # 包含 i 和 j 和 k\n",
    "    yHat = np.logical_and(np.logical_and(trainX[:, i] == 1, trainX[:, j] == 1), trainX[:, k] == 1)\n",
    "    numErrors.append(sum(abs(yHat - trainY)))\n",
    "    rules.append([i, j, k, 1, 1, 1])\n",
    "\n",
    "timeElapsed = time.time() - startTime\n",
    "print('Time elapsed = ' + str(round(timeElapsed, 3)) + ' seconds.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218858"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(np.array(numErrors))\n",
    "#返回numErrors中最小值的索引。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 40, 53, 0, 1, 0]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[218858]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 引入NumPy库，约定别名为np\n",
    "import numpy as np\n",
    "\n",
    "# 定义lambda函数，参数为X\n",
    "rule = lambda X: np.logical_and(\n",
    "    # 对第9列进行条件判断，是否等于0\n",
    "    np.logical_and(X[:, 9] == 0,\n",
    "    \n",
    "    # 对第40列进行条件判断，是否等于1\n",
    "    X[:, 40] == 1),\n",
    "    \n",
    "    # 对第53列进行条件判断，是否等于0\n",
    "    X[:, 53] == 0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入NumPy库，约定别名为np\n",
    "import numpy as np\n",
    "\n",
    "# 导入绘图相关库\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建一个包含81个零的NumPy数组\n",
    "ruleVector = np.zeros(81)\n",
    "\n",
    "# 在第9、40和53个元素分别赋值为-1、1和-1\n",
    "ruleVector[9] = -1\n",
    "ruleVector[40] = 1\n",
    "ruleVector[53] = -1\n",
    "\n",
    "# 创建一个6x6大小的图表对象\n",
    "fig = plt.figure(0, (6, 6))\n",
    "\n",
    "# 将规则向量重塑为9x9的矩阵，并以图像形式显示\n",
    "plt.imshow(ruleVector.reshape(9, 9), interpolation='none', cmap='RdBu')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制错误数作为训练集上性能的函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入绘图相关库\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建一个大小为12x6的图表对象\n",
    "fig = plt.figure(0, (12, 6))\n",
    "\n",
    "# 绘制排序后的错误数量曲线，设置线宽为2\n",
    "plt.plot(sorted(numErrors), linewidth=2)\n",
    "\n",
    "# 添加网格线\n",
    "plt.grid(True)\n",
    "\n",
    "# 设置横轴标签为 'Rule'，字体大小为18\n",
    "plt.xlabel('Rule', fontsize=18)\n",
    "\n",
    "# 设置纵轴标签为 'Number of Errors'，字体大小为18\n",
    "plt.ylabel('Number of Errors', fontsize=18)\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 现在，训练和测试前100条规则的错误"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 导入NumPy库，约定别名为np\n",
    "import numpy as np\n",
    "\n",
    "# 使用argsort对错误数量进行排序，返回排序后的索引\n",
    "sI = np.argsort(np.array(numErrors))\n",
    "\n",
    "# 初始化空列表，用于存储训练和测试错误\n",
    "testingErrors = []\n",
    "trainingErrors = []\n",
    "\n",
    "# 遍历排序后的前100个规则\n",
    "for i in range(100):\n",
    "    # 获取当前规则的索引\n",
    "    r = rules[sI[i]]\n",
    "    \n",
    "    # 创建lambda函数，根据规则r判断样本\n",
    "    rule = lambda X: np.logical_and(\n",
    "        np.logical_and(X[:, r[0]] == r[3], X[:, r[1]] == r[4]),\n",
    "        X[:, r[2]] == r[5]\n",
    "    )\n",
    "    \n",
    "    # 计算训练集中的错误数量，将其添加到训练错误列表中\n",
    "    trainingErrors.append(np.sum(np.abs(rule(trainX) - trainY)))\n",
    "    \n",
    "    # 计算测试集中的错误数量，将其添加到测试错误列表中\n",
    "    testingErrors.append(np.sum(np.abs(rule(testX) - testY)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入绘图相关库\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建一个大小为12x6的图表对象\n",
    "fig = plt.figure(0, (12, 6))\n",
    "\n",
    "# 绘制训练错误和测试错误曲线，设置线型为蓝色圆点线和绿色圆点线，线宽为2\n",
    "plt.plot(trainingErrors, 'bo-', linewidth=2, markeredgecolor='none')\n",
    "plt.plot(testingErrors, 'go-', linewidth=2, markeredgecolor='none')\n",
    "\n",
    "# 添加网格线\n",
    "plt.grid(True)\n",
    "\n",
    "# 设置横轴标签为 'Rule'，字体大小为18\n",
    "plt.xlabel('Rule', fontsize=18)\n",
    "\n",
    "# 设置纵轴标签为 'Number of Errors'，字体大小为18\n",
    "plt.ylabel('Number of Errors', fontsize=18)\n",
    "\n",
    "# 添加图例，标注训练集和测试集\n",
    "plt.legend(['On Training Set', 'On Testing Set'], loc=4)\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "depth-camera",
   "language": "python",
   "name": "depth-camera"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": false,
   "toc_section_display": "block",
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
